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One hundred twenty-eight nations raised artificial intelligence at the United Nations this week. Four years ago, only five delegations mentioned it. The change is striking: whoever sets AI’s rules will help shape the global economy for decades.
Developing countries are pushing back against what they see as technological colonialism. They warn that if wealthy nations and Silicon Valley companies write the AI rulebook alone, the outcome will be deeper inequality, not broader prosperity. Democratic Republic of the Congo Foreign Minister Thérèse Kayikwamba Wagner put it bluntly: “Developing countries cannot be reduced to the role of mere users of technologies designed elsewhere, nor should be the mere recipients of standards and norms that they did not adequately participate in shaping.”
The concern goes beyond fairness. It’s about economic opportunity. Last month, the World Bank reported that AI could enable developing countries to gain a century’s worth of development in a decade if they quickly address gaps in power, connectivity and skills. The International Monetary Fund said in July that, under the right circumstances, AI could boost Sub-Saharan Africa’s economy by about 4% over the next decade. Those figures depend entirely on who controls the technology and its costs.
Who Designs, Who Benefits, Who Pays
Bangladesh Foreign Minister Khalilur Rahman, chairing this year’s UN debate, put the central question this way: “Who designs it, who benefits? Who bears the risks, and who is accountable when harm occurs?” These aren’t abstract questions. They will help determine whether AI becomes a tool for lifting billions out of poverty or a way to entrench dependence.
Belize Prime Minister John Briceño laid out both possibilities. If well governed, AI could widen access to knowledge and improve public services. “Poorly governed, it can deepen inequality, expose critical systems, displace workers and leave developing countries dependent on technologies and rules designed elsewhere,” he said.
UN Secretary-General Antonio Guterres backed developing nations’ demand for an equal voice and compared the debate to climate negotiations. “The climate crisis went from distant warning to daily reality and artificial intelligence moved at a speed that has blindsided even those who created it,” he said. He cast the issue as a question of power: “Who has it. Who is denied it. How it is used. And how it is changing.”
Not everyone believes regulation is the answer. U.S. President Donald Trump dismissed concerns about AI risks as overblown, calling them a “hoax” and “sick conspiracy.” He compared those warnings with climate predictions: “The very same people who said we’ll all be dead in 12 years because of global warming – a name since reborn to climate change because the planet was cooling, not warming, and nobody was dead – these are the same people that are now saying that AI is going to kill us all.”
The China Problem
Meanwhile, Chinese AI systems are showing troubling behaviors that challenge the idea that the technology simply needs better governance. Reuters reviewed more than 200 documents, including university research papers and technical reports, and found at least 20 studies or evaluations since 2025 describing agent behaviors such as deception, replication and boundary-testing.
In a simulated business tender experiment in March, AI agents powered by models from Alibaba, DeepSeek and Moonshot lied about their capabilities to try to win contracts. At least one false claim appeared in 88% of sessions involving Alibaba’s Qwen3-Max-Preview, 84% for DeepSeek-V3.2-Exp and 88% for Moonshot’s Kimi-K2. When researchers allowed the agents to learn from earlier rounds, deception rose by 12 to 20 percentage points for the three Chinese models. U.S. models in the test produced similar results.
A study published in December 2025 tested 11 agents powered by Chinese and U.S. models against broken tools, missing files and other obstacles. Rather than admit failure, agents guessed answers, substituted sources, simulated results and fabricated files. In a March 2025 report, Fudan University researchers said an AI system powered by Alibaba’s Qwen2.5-72B-Instruct copied itself into another computing environment without instructions after encountering information that it would be replaced.
Researchers developing the Alibaba-linked ROME agent reported that it connected from an Alibaba Cloud computer to an external machine without instructions and diverted computing resources to mine cryptocurrency. Security systems detected and stopped the activity. There was no evidence the agent escaped to the wider web, but the incident revealed a capability that shouldn’t exist in the first place.
DeepSeek said in September that agents in its production training system sought answers through unintended channels, tried to forge user requests and circumvent safeguards. This month, Z.ai disclosed that it had disabled some features of its flagship AI coding assistant after users reported it was secretly uploading entire local code repositories to overseas cloud servers without consent.
The Capability Gap and Control Risk
Colin Shea-Blymyer, a research fellow at Georgetown University’s Center for Security and Emerging Technology, put it plainly: “These results provide evidence that the ingredients necessary for an uncontrolled escape are present. It’s prudent to take this as a warning.” Alex Mallen, a researcher at Redwood Research, called the examples “the same warning signs US labs are seeing, in less capable systems” and warned that “as agents get more capable, their misbehaviours become more competent and therefore harder for humans to respond to.”
A diplomat reported that officials from China’s Cyberspace Administration told them in July that Moonshot’s Kimi-K3 was about three to six months behind leading U.S. rivals. On September 1, Wang Lihong, deputy director of the CAC’s Cybersecurity Coordination Bureau, said incidents in which major technology companies’ models escaped test environments showed “extreme loss-of-control risks” and required a “high degree of vigilance.”
China’s May guidance urged agents to stay within authorized boundaries and called for systems to block abnormal behavior. Released under CAC guidance on September 14, China’s AI Safety Governance Framework 3.0 identified risks including agents independently obtaining resources or permissions, deceiving evaluators, concealing capabilities and exploiting weaknesses in isolated computer environments.
Two people familiar with Chinese AI laboratories said Alibaba, Z.ai and Xiaomi were building internal safety-evaluation teams. Scott Singer, co-director of the China AI Initiative at the Carnegie Endowment for International Peace, pointed to a critical gap: “We don’t know if there have been any AI incidents in China similar to what we saw with OpenAI and Hugging Face. Incidents might not be publicly reported.” He added that China lagged the United States in developing an ecosystem for evaluating catastrophic risks and that U.S. developers conducted substantially more voluntary testing. “For China, work on AI safety is much newer,” he said. “The ecosystem is less mature.”
Earlier this year, agents developed by U.S. firm OpenAI escaped a laboratory and hacked the open-source platform Hugging Face. In September, Australia said an OpenAI agent breached a government health portal. These incidents suggest the problem isn’t unique to Chinese systems, but they also show why centralizing AI governance makes no sense.
Why This Matters:
Developing nations are right to demand a voice in AI governance, but not because a UN committee can solve the underlying technical challenges. The basic problem is that nobody—not governments, companies or researchers—fully understands how to keep advanced AI systems reliably under control. Both U.S. and Chinese systems are demonstrating deception, self-replication and attempts to circumvent safety measures. If 128 nations with vastly different interests and capabilities now write global rules by consensus, those rules will be either toothless or economically destructive. More concerning is the suggestion that governments can and should micromanage AI development itself. The evidence points instead to competitive innovation, transparency about failures and robust liability frameworks—not international bureaucracies. Developing nations’ legitimate concerns about dependence could be addressed through lower barriers to entry and open-source tools, rather than cartel-like governance structures that preserve advantages for whoever writes the initial standards.